Silent-speech enhancement using body-conducted vocal-tract resonance signals

The physical characteristics of weak body-conducted vocal-tract resonance signals called non-audible murmur (NAM) and the acoustic characteristics of three sensors developed for detecting these signals have been investigated. NAM signals attenuate 50 dB at 1 kHz; this attenuation consists of 30-dB full-range attenuation due to air-to-body transmission loss and 10 dB/octave spectral decay due to a sound propagation loss within the body. These characteristics agree with the spectral characteristics of measured NAM signals. The sensors have a sensitivity of between 41 and 58 dB [V/Pa] at I kHz, and the mean signal-to-noise ratio of the detected signals was 15 dB. On the basis of these investigations, three types of silent-speech enhancement systems were developed: (1) simple, direct amplification of weak vocal-tract resonance signals using a wired urethane-elastomer NAM microphone, (2) simple, direct amplification using a wireless urethane-elastomer-duplex NAM microphone, and (3) transformation of the weak vocal-tract resonance signals sensed by a soft-silicone NAM microphone into whispered speech using statistical conversion. Field testing of the systems showed that they enable voice impaired people to communicate verbally using body-conducted vocal-tract resonance signals. Listening tests demonstrated that weak body-conducted vocal-tract resonance sounds can be transformed into intelligible whispered speech sounds. Using these systems, people with voice impairments can re-acquire speech communication with less effort. (C) 2009 Elsevier B.V. All rights reserved.ArticleSPEECH COMMUNICATION. 52(4):301-313 (2010)journal articl

Finding the Most Uniform Changes in Vowel Polygon Caused by Psychological Stress

Using vowel polygons, exactly their parameters, is chosen as the criterion for achievement of differences between normal state of speaker and relevant speech under real psychological stress. All results were experimentally obtained by created software for vowel polygon analysis applied on ExamStress database. Selected 6 methods based on cross-correlation of different features were classified by the coefficient of variation and for each individual vowel polygon, the efficiency coefficient marking the most significant and uniform differences between stressed and normal speech were calculated. As the best method for observing generated differences resulted method considered mean of cross correlation values received for difference area value with vector length and angle parameter couples. Generally, best results for stress detection are achieved by vowel triangles created by /i/-/o/-/u/ and /a/-/i/-/o/ vowel triangles in formant planes containing the fifth formant F5 combined with other formants

Efficient, end-to-end and self-supervised methods for speech processing and generation

Deep learning has affected the speech processing and generation fields in many directions. First, end-to-end architectures allow the direct injection and synthesis of waveform samples. Secondly, the exploration of efficient solutions allow to implement these systems in computationally restricted environments, like smartphones. Finally, the latest trends exploit audio-visual data with least supervision. In this thesis these three directions are explored. Firstly, we propose the use of recent pseudo-recurrent structures, like self-attention models and quasi-recurrent networks, to build acoustic models for text-to-speech. The proposed system, QLAD, turns out to synthesize faster on CPU and GPU than its recurrent counterpart whilst preserving the good synthesis quality level, which is competitive with state of the art vocoder-based models. Then, a generative adversarial network is proposed for speech enhancement, named SEGAN. This model works as a speech-to-speech conversion system in time-domain, where a single inference operation is needed for all samples to operate through a fully convolutional structure. This implies an increment in modeling efficiency with respect to other existing models, which are auto-regressive and also work in time-domain. SEGAN achieves prominent results in noise supression and preservation of speech naturalness and intelligibility when compared to the other classic and deep regression based systems. We also show that SEGAN is efficient in transferring its operations to new languages and noises. A SEGAN trained for English performs similarly to this language on Catalan and Korean with only 24 seconds of adaptation data. Finally, we unveil the generative capacity of the model to recover signals from several distortions. We hence propose the concept of generalized speech enhancement. First, the model proofs to be effective to recover voiced speech from whispered one. Then the model is scaled up to solve other distortions that require a recomposition of damaged parts of the signal, like extending the bandwidth or recovering lost temporal sections, among others. The model improves by including additional acoustic losses in a multi-task setup to impose a relevant perceptual weighting on the generated result. Moreover, a two-step training schedule is also proposed to stabilize the adversarial training after the addition of such losses, and both components boost SEGAN's performance across distortions.Finally, we propose a problem-agnostic speech encoder, named PASE, together with the framework to train it. PASE is a fully convolutional network that yields compact representations from speech waveforms. These representations contain abstract information like the speaker identity, the prosodic features or the spoken contents. A self-supervised framework is also proposed to train this encoder, which suposes a new step towards unsupervised learning for speech processing. Once the encoder is trained, it can be exported to solve different tasks that require speech as input. We first explore the performance of PASE codes to solve speaker recognition, emotion recognition and speech recognition. PASE works competitively well compared to well-designed classic features in these tasks, specially after some supervised adaptation. Finally, PASE also provides good descriptors of identity for multi-speaker modeling in text-to-speech, which is advantageous to model novel identities without retraining the model.L'aprenentatge profund ha afectat els camps de processament i generació de la parla en vàries direccions. Primer, les arquitectures fi-a-fi permeten la injecció i síntesi de mostres temporals directament. D'altra banda, amb l'exploració de solucions eficients permet l'aplicació d'aquests sistemes en entorns de computació restringida, com els telèfons intel·ligents. Finalment, les darreres tendències exploren les dades d'àudio i veu per derivar-ne representacions amb la mínima supervisió. En aquesta tesi precisament s'exploren aquestes tres direccions. Primer de tot, es proposa l'ús d'estructures pseudo-recurrents recents, com els models d’auto atenció i les xarxes quasi-recurrents, per a construir models acústics text-a-veu. Així, el sistema QLAD proposat en aquest treball sintetitza més ràpid en CPU i GPU que el seu homòleg recurrent, preservant el mateix nivell de qualitat de síntesi, competitiu amb l'estat de l'art en models basats en vocoder. A continuació es proposa un model de xarxa adversària generativa per a millora de veu, anomenat SEGAN. Aquest model fa conversions de veu-a-veu en temps amb una sola operació d'inferència sobre una estructura purament convolucional. Això implica un increment en l'eficiència respecte altres models existents auto regressius i que també treballen en el domini temporal. La SEGAN aconsegueix resultats prominents d'extracció de soroll i preservació de la naturalitat i la intel·ligibilitat de la veu comparat amb altres sistemes clàssics i models regressius basats en xarxes neuronals profundes en espectre. També es demostra que la SEGAN és eficient transferint les seves operacions a nous llenguatges i sorolls. Així, un model SEGAN entrenat en Anglès aconsegueix un rendiment comparable a aquesta llengua quan el transferim al català o al coreà amb només 24 segons de dades d'adaptació. Finalment, explorem l'ús de tota la capacitat generativa del model i l’apliquem a recuperació de senyals de veu malmeses per vàries distorsions severes. Això ho anomenem millora de la parla generalitzada. Primer, el model demostra ser efectiu per a la tasca de recuperació de senyal sonoritzat a partir de senyal xiuxiuejat. Posteriorment, el model escala a poder resoldre altres distorsions que requereixen una reconstrucció de parts del senyal que s’han malmès, com extensió d’ample de banda i recuperació de seccions temporals perdudes, entre d’altres. En aquesta última aplicació del model, el fet d’incloure funcions de pèrdua acústicament rellevants incrementa la naturalitat del resultat final, en una estructura multi-tasca que prediu característiques acústiques a la sortida de la xarxa discriminadora de la nostra GAN. També es proposa fer un entrenament en dues etapes del sistema SEGAN, el qual mostra un increment significatiu de l’equilibri en la sinèrgia adversària i la qualitat generada finalment després d’afegir les funcions acústiques. Finalment, proposem un codificador de veu agnòstic al problema, anomenat PASE, juntament amb el conjunt d’eines per entrenar-lo. El PASE és un sistema purament convolucional que crea representacions compactes de trames de veu. Aquestes representacions contenen informació abstracta com identitat del parlant, les característiques prosòdiques i els continguts lingüístics. També es proposa un entorn auto-supervisat multi-tasca per tal d’entrenar aquest sistema, el qual suposa un avenç en el terreny de l’aprenentatge no supervisat en l’àmbit del processament de la parla. Una vegada el codificador esta entrenat, es pot exportar per a solventar diferents tasques que requereixin tenir senyals de veu a l’entrada. Primer explorem el rendiment d’aquest codificador per a solventar tasques de reconeixement del parlant, de l’emoció i de la parla, mostrant-se efectiu especialment si s’ajusta la representació de manera supervisada amb un conjunt de dades d’adaptació

Efficient, end-to-end and self-supervised methods for speech processing and generation

Deep learning has affected the speech processing and generation fields in many directions. First, end-to-end architectures allow the direct injection and synthesis of waveform samples. Secondly, the exploration of efficient solutions allow to implement these systems in computationally restricted environments, like smartphones. Finally, the latest trends exploit audio-visual data with least supervision. In this thesis these three directions are explored. Firstly, we propose the use of recent pseudo-recurrent structures, like self-attention models and quasi-recurrent networks, to build acoustic models for text-to-speech. The proposed system, QLAD, turns out to synthesize faster on CPU and GPU than its recurrent counterpart whilst preserving the good synthesis quality level, which is competitive with state of the art vocoder-based models. Then, a generative adversarial network is proposed for speech enhancement, named SEGAN. This model works as a speech-to-speech conversion system in time-domain, where a single inference operation is needed for all samples to operate through a fully convolutional structure. This implies an increment in modeling efficiency with respect to other existing models, which are auto-regressive and also work in time-domain. SEGAN achieves prominent results in noise supression and preservation of speech naturalness and intelligibility when compared to the other classic and deep regression based systems. We also show that SEGAN is efficient in transferring its operations to new languages and noises. A SEGAN trained for English performs similarly to this language on Catalan and Korean with only 24 seconds of adaptation data. Finally, we unveil the generative capacity of the model to recover signals from several distortions. We hence propose the concept of generalized speech enhancement. First, the model proofs to be effective to recover voiced speech from whispered one. Then the model is scaled up to solve other distortions that require a recomposition of damaged parts of the signal, like extending the bandwidth or recovering lost temporal sections, among others. The model improves by including additional acoustic losses in a multi-task setup to impose a relevant perceptual weighting on the generated result. Moreover, a two-step training schedule is also proposed to stabilize the adversarial training after the addition of such losses, and both components boost SEGAN's performance across distortions.Finally, we propose a problem-agnostic speech encoder, named PASE, together with the framework to train it. PASE is a fully convolutional network that yields compact representations from speech waveforms. These representations contain abstract information like the speaker identity, the prosodic features or the spoken contents. A self-supervised framework is also proposed to train this encoder, which suposes a new step towards unsupervised learning for speech processing. Once the encoder is trained, it can be exported to solve different tasks that require speech as input. We first explore the performance of PASE codes to solve speaker recognition, emotion recognition and speech recognition. PASE works competitively well compared to well-designed classic features in these tasks, specially after some supervised adaptation. Finally, PASE also provides good descriptors of identity for multi-speaker modeling in text-to-speech, which is advantageous to model novel identities without retraining the model.L'aprenentatge profund ha afectat els camps de processament i generació de la parla en vàries direccions. Primer, les arquitectures fi-a-fi permeten la injecció i síntesi de mostres temporals directament. D'altra banda, amb l'exploració de solucions eficients permet l'aplicació d'aquests sistemes en entorns de computació restringida, com els telèfons intel·ligents. Finalment, les darreres tendències exploren les dades d'àudio i veu per derivar-ne representacions amb la mínima supervisió. En aquesta tesi precisament s'exploren aquestes tres direccions. Primer de tot, es proposa l'ús d'estructures pseudo-recurrents recents, com els models d’auto atenció i les xarxes quasi-recurrents, per a construir models acústics text-a-veu. Així, el sistema QLAD proposat en aquest treball sintetitza més ràpid en CPU i GPU que el seu homòleg recurrent, preservant el mateix nivell de qualitat de síntesi, competitiu amb l'estat de l'art en models basats en vocoder. A continuació es proposa un model de xarxa adversària generativa per a millora de veu, anomenat SEGAN. Aquest model fa conversions de veu-a-veu en temps amb una sola operació d'inferència sobre una estructura purament convolucional. Això implica un increment en l'eficiència respecte altres models existents auto regressius i que també treballen en el domini temporal. La SEGAN aconsegueix resultats prominents d'extracció de soroll i preservació de la naturalitat i la intel·ligibilitat de la veu comparat amb altres sistemes clàssics i models regressius basats en xarxes neuronals profundes en espectre. També es demostra que la SEGAN és eficient transferint les seves operacions a nous llenguatges i sorolls. Així, un model SEGAN entrenat en Anglès aconsegueix un rendiment comparable a aquesta llengua quan el transferim al català o al coreà amb només 24 segons de dades d'adaptació. Finalment, explorem l'ús de tota la capacitat generativa del model i l’apliquem a recuperació de senyals de veu malmeses per vàries distorsions severes. Això ho anomenem millora de la parla generalitzada. Primer, el model demostra ser efectiu per a la tasca de recuperació de senyal sonoritzat a partir de senyal xiuxiuejat. Posteriorment, el model escala a poder resoldre altres distorsions que requereixen una reconstrucció de parts del senyal que s’han malmès, com extensió d’ample de banda i recuperació de seccions temporals perdudes, entre d’altres. En aquesta última aplicació del model, el fet d’incloure funcions de pèrdua acústicament rellevants incrementa la naturalitat del resultat final, en una estructura multi-tasca que prediu característiques acústiques a la sortida de la xarxa discriminadora de la nostra GAN. També es proposa fer un entrenament en dues etapes del sistema SEGAN, el qual mostra un increment significatiu de l’equilibri en la sinèrgia adversària i la qualitat generada finalment després d’afegir les funcions acústiques. Finalment, proposem un codificador de veu agnòstic al problema, anomenat PASE, juntament amb el conjunt d’eines per entrenar-lo. El PASE és un sistema purament convolucional que crea representacions compactes de trames de veu. Aquestes representacions contenen informació abstracta com identitat del parlant, les característiques prosòdiques i els continguts lingüístics. També es proposa un entorn auto-supervisat multi-tasca per tal d’entrenar aquest sistema, el qual suposa un avenç en el terreny de l’aprenentatge no supervisat en l’àmbit del processament de la parla. Una vegada el codificador esta entrenat, es pot exportar per a solventar diferents tasques que requereixin tenir senyals de veu a l’entrada. Primer explorem el rendiment d’aquest codificador per a solventar tasques de reconeixement del parlant, de l’emoció i de la parla, mostrant-se efectiu especialment si s’ajusta la representació de manera supervisada amb un conjunt de dades d’adaptació.Postprint (published version

Exploitation of Phase-Based Features for Whispered Speech Emotion Recognition

Features for speech emotion recognition are usually dominated by the spectral magnitude information while they ignore the use of the phase spectrum because of the difficulty of properly interpreting it. Motivated by recent successes of phase-based features for speech processing, this paper investigates the effectiveness of phase information for whispered speech emotion recognition. We select two types of phase-based features (i.e., modified group delay features and all-pole group delay features), both which have shown wide applicability to all sorts of different speech analysis and are now studied in whispered speech emotion recognition. When exploiting these features, we propose a new speech emotion recognition framework, employing outer product in combination with power and L2 normalization. The according technique encodes any variable length sequence of the phase-based features into a fixed dimension vector regardless of the length of the input sequence. The resulting representation is fed to train a classification model with a linear kernel classifier. Experimental results on the Geneva Whispered Emotion Corpus database, including normal and whispered phonation, demonstrate the effectiveness of the proposed method when compared with other modern systems. It is also shown that, combining phase information with magnitude information could significantly improve performance over the common systems solely adopting magnitude information

A Comparison of Decision Tree Classifiers for Automatic Diagnosis of Speech Recognition Errors

Present speech recognition systems are becoming more complex due to technology advances, optimizations and special requirements such as small computation and memory footprints. Proper handling of system failures can be seen as a kind of fault diagnosis. Motivated by the success of decision tree diagnosis in other scientific fields and by their successful application in speech recognition in the last decade, we contribute to the topic mainly in terms of comparison of different types of decision trees. Five styles are examined: CART (testing three different splitting criteria), C4.5, and then Minimum Message Length (MML), strict MML and Bayesian styles decision trees. We apply these techniques to data of computer speech recognition fed by intrinsically variable speech. We conclude that for this task, CART technique outperforms C4.5 in terms of better classification for ASR failures

Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this book is substantially improved in terms of accuracy, flexibility, and robustness

Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this book is substantially improved in terms of accuracy, flexibility, and robustness